/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *    http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
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package org.apache.predictionio.e2.evaluation

import scala.reflect.ClassTag
import org.apache.spark.rdd.RDD

/** Common helper functions */
object CommonHelperFunctions {

  /** Split a data set into evalK folds for crossvalidation.
    * Apply to data sets supplied to evaluation.
    *
    * @tparam D Data point class.
    * @tparam TD Training data class.
    * @tparam EI Evaluation Info class.
    * @tparam Q Input query class.
    * @tparam A Actual value class.
    */

  def splitData[D: ClassTag, TD, EI, Q, A](

     evalK: Int,
     dataset: RDD[D],
     evaluatorInfo: EI,
     trainingDataCreator: RDD[D] => TD,
     queryCreator: D => Q,
     actualCreator: D => A): Seq[(TD, EI, RDD[(Q, A)])] = {

    val indexedPoints = dataset.zipWithIndex

    def selectPoint(foldIdx: Int, pt: D, idx: Long, k: Int, isTraining: Boolean): Option[D] = {
      if ((idx % k == foldIdx) ^ isTraining) Some(pt)
      else None
    }

    (0 until evalK).map { foldIdx =>
      val trainingPoints = indexedPoints.flatMap { case(pt, idx) =>
        selectPoint(foldIdx, pt, idx, evalK, true)
      }
      val testingPoints = indexedPoints.flatMap { case(pt, idx) =>
        selectPoint(foldIdx, pt, idx, evalK, false)
      }

      (
        trainingDataCreator(trainingPoints),
        evaluatorInfo,
        testingPoints.map { d => (queryCreator(d), actualCreator(d)) }
      )
    }
  }
}
